code: def test_eval(self):
      register_all_modules()
      dataset = OpenImagesDataset(
          data_root="tests/data/OpenImages/",
          ann_file="annotations/oidv6-train-annotations-bbox.csv",
          data_prefix=dict(img="OpenImages/train/"),
          label_file="annotations/class-descriptions-boxable.csv",
          hierarchy_file="annotations/bbox_labels_600_hierarchy.json",
          meta_file="annotations/image-metas.pkl",
          pipeline=[
              dict(type="LoadAnnotations", with_bbox=True),
              dict(
                  type="PackDetInputs",
                  meta_keys=("img_id", "img_path", "instances"),
              ),
          ],
      )
      dataset.full_init()
      data_sample = dataset[0]["data_samples"].to_dict()
      data_sample["pred_instances"] = self._create_dummy_results()
  
      metric = OpenImagesMetric()
      metric.dataset_meta = dataset.metainfo
      metric.process({}, [data_sample])
      results = metric.evaluate(size=len(dataset))
      targets = {"openimages/AP50": 1.0, "openimages/mAP": 1.0}
      self.assertDictEqual(results, targets)
  
      # test multi-threshold
      metric = OpenImagesMetric(
          iou_thrs=[0.1, 0.5], ioa_thrs=[0.1, 0.5]
      )
      metric.dataset_meta = dataset.metainfo
      metric.process({}, [data_sample])
      results = metric.evaluate(size=len(dataset))
      targets = {
          "openimages/AP10": 1.0,
          "openimages/AP50": 1.0,
          "openimages/mAP": 1.0,
      }
      self.assertDictEqual(results, targets)
  
file_path: mmdetection/tests/test_evaluation/test_metrics/test_openimages_metric.py
imports: import unittest
  import numpy
  import torch
  from mmdet.datasets import OpenImagesDataset
  from mmdet.evaluation import OpenImagesMetric
  from mmdet.utils import register_all_modules
label_desc: Use image node_id 3 for calling the TestOpenImagesMetric obj's underlying member method code with example usage: obj.test_eval() without return types